GraphNews

In this work, we achieve perfect neural execution of several algorithms by forcing the node and edge representations to be from a fixed finite set. Also, the proposed architectural choice allows us to prove the correctness of the learned algorithms for any test data.

Author Dan Selman shows how easy it is to implement convert natural language text to nodes and edges in a knowledge graph using a new class and method in his demonstration project.

Implementing Semantic Data Products: A Comprehensive Blueprint for Success As we conclude our series on semantic data products, let's put all the pieces… | 15 comments on LinkedIn

Knowledge Graph / Concept Model: Same or Different? In my understanding when people say 'knowledge graph' they are usually talking about something OWL/RDF-ish,… | 42 comments on LinkedIn

💡 The relevance, trustworthiness and quality of AI and #GenAI applications is increasingly dependent on the quality of enterprise private data and documents…

1. Where does a knowledge graph fit in an enterprise data architecture? A knowledge graph typically sits above data entry/storage systems and below data consumption/visualization systems.

Just learned yesterday that GNOME has been using SPARQL in desktop search for quite a while—and I had no idea! 😲 Turns out their tool, "Tracker", is powered…

🚀 PyG 2.6 is here! 🎉 We’re excited to announce the release of PyG 2.6.0, packed with incredible updates for graph learning! Here’s a quick rundown of what’s… | 14 comments on LinkedIn

Your first ontology doesn’t need to be an ontology. Here’s what I mean. Combining ontologies with knowledge graphs is a powerful approach. But implementing… | 21 comments on LinkedIn

Elevating RAG accuracy and performance by structuring long documents into explorable graphs and implementing graph-based agent systems

Graph neural networks (GNNs) are a type of neural network capable of learning on graph-structured data. However, training GNNs on large-scale graphs is challenging due to iterative aggregations of...

Hey Knowledge Graph friends! As I imagine some of you are, I've been a bit annoyed that many "Knowledge Graph and AI" demos and tools—while definitely… | 13 comments on LinkedIn

How do you maintain an ontology over time? Today, I had a wonderful meeting with Kurt Cagle about ontologies, AI, and beyond. We spent some time on this… | 27 comments on LinkedIn

Design patterns for improving GenAI applications with a graph.

Finding the perfect GraphRAG pattern demands experimentation with different retrieval patterns

Pushing AI On-Device with GraphRAG 🚀. Contribute to trustgraph-ai/trustgraph development by creating an account on GitHub.

#Alhamdulillah, Our iText2KG has achieved over 300 stars and 27 forks in just 10 days after its release, and it is currently ranked among the top 12 trending…

Your first ontology doesn’t need to be an ontology. Here’s what I mean. Combining ontologies with knowledge graphs is a powerful approach. But implementing…

Web Ontology Language (OWL) emerges as a superior choice for knowledge representation in generative AI, offering unparalleled expressiveness, reasoning capabilities, and semantic richness. By leveraging OWL-based knowledge graphs, AI systems can generate more accurate, context-aware, and nuanced outputs across diverse ...

As the volume of digital content increases, the ability to manage it becomes more important. Taxonomy and metadata are vital to finding products, conducting scientific research, and keeping track of organizational information. They also enable a wide variety of analytics on unstructured data. We can see the results of a well-designed taxonomy, but behind the scenes, there's a lot more going on than meets the eye.

My presentation at the "First International Workshop on Scaling Knowledge Graphs for Industry" at the SEMANTICS 2024 Conference in Amsterdam was focused on the…

DOM Graph RAG: A semantic content architecture for neuro-symbolic RAG

Introduction

i want to share the actual steps we’re using to generate text to sql through an ontology instead of an LLM [explained with a library analogy]: 𝟭… | 15 comments on LinkedIn

Covering the advanced RAG options in R2R

The growing ubiquity of relational data structured as graphs has underscored the need for graph learning models with exceptional generalization capabilities. However, current approaches often...

✨ Operationalizing the information architecture 👇 There are three main ways to operationalize the information architecture, depending on how the data plane… | 14 comments on LinkedIn

A Step-by-Step Tutorial on Implementing Retrieval-Augmented Generation (RAG), Semantic Search, and Recommendations

"From Semiotic to Digital Enterprise Semantic Interoperability? Translation in english with some completion of my last article in French. Enterprises are…